基于压缩算法对酒蒸子养操作的温度检测方法的研究
Xiaolian Liu1, Shaopeng Gong1, Xiangxu Hua1
1School of Mechanical Engineering, Taiyuan University of Science and Technology, Taiyuan, 030024, China.
Scientific reports
|June 10, 2024
概括
本研究介绍了YOLOv5ns-DP,这是一种轻量级模型,用于检测蒸过程中发酵谷物表面的温度变化. 它通过在溢出之前准确地定位液体蒸汽来提高检测准确度和速度.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 化学工程是化学工程的重要组成部分.
背景情况:
- 目前用于在蒸料中检测液体蒸汽的方法往往会延迟,并且缺乏精确的位置识别.
- 当前的计算模型可能是资源密集型,阻碍实时部署和性能.
研究的目的:
- 开发一种轻量级和高效的模型,用于准确,实时检测发酵谷物表面的温度变化.
- 在蒸料过程中提高液体蒸汽检测的精度和速度.
主要方法:
- 建立了发酵谷物表面的红外图像数据集.
- 融合了YOLOv5n与知识蒸和模型修剪算法,创建了YOLOv5ns-DP模型.
- 在参数,计算负载 (GLOPs),模型大小和平均精度 (mAP) 方面评估模型的性能.
主要成果:
- 在YOLOv5ns-DP模型中显示了显著的减少:参数减少了28.6%,GLOP减少了16.5%,模型大小减少了26.4%.
- 在mAP中取得了0.6的改善,表明检测准确度提高.
- 该模型有效地预测并准确地定位液体蒸汽,提高检测精度和速度.
结论:
- 拟议的YOLOv5ns-DP模型比现有的实时监测发酵谷物的温度的方法有了显著的改进.
- 这种轻量级的方法提高了蒸过程的计算效率和部署性能.
- 精确和早期检测液体蒸汽对于优化蒸料过程至关重要.
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